Polarity lexicons, an important resource for automatic sentiment analysis in text, are expensive to build manually, and they ignore the complexity of how sentiment can be expressed (e.g. implicitly, using factual statements that entail a sentiment). This project investigates a method for automatically deriving polarity lexicons from large corpora, allowing more complex representations than single lemmas.